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ffcmw: The Fastest Fuzzy C-Means in the West!

version (3.96 KB) by Marco Cococcioni
A fast implementation of the well-known fuzzy c-means clustering algorithm


Updated 03 Jul 2019

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When you need to clusterize data, fuzzy c-means is an appealing candidate, being it more robust and stable than the k-means clustering algorithm. This implementation is faster than that found in the Fuzzy Logic Toolbox (fcm.m). In addition, you can run it without having to buy the FL Toolbox. With this entry I want to stimulate the involvment of other users, to further speedup it and with the ultimate goal to eventually find the TRUE fastest fcm in the West!!

Cite As

Marco Cococcioni (2021). ffcmw: The Fastest Fuzzy C-Means in the West! (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (10)

David Franco

How can I use weights for each data point?

Mitchell Krouss

Jie Feng

well done!

David Franco

heba sal

thank you so much, please page number that explains the algorithm

Marco Cococcioni

Many people are asking me a reference to my fuzzy c-means implementation.
This implementation is aligned with the description provided in the following book:

Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

In addition, my implementation perform the same computations done by Matlab implementation by function fcm.m.

The only difference with fcm.m is that my version heavily uses Matlab vectorized functions. Only on for loop is used (the outher one).
This is way it is faster.

nassima bousahba

many thanks

Inyoung Bae

Hello. It works well. If you don't mind, can I get a paper or literature that describes this algorithm?

Marco Cococcioni

Hi Tan Yuki, I guess it can. But I need to better understood your problem.
Please drop me an email privately.
You can find my email address here:

Best Regards,


tan yuki

Hi. it really work very fast. Can this be implement on interest object's image rather than sample points?

MATLAB Release Compatibility
Created with R2019a
Compatible with R2011a and later releases
Platform Compatibility
Windows macOS Linux

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